Article published in:
Register Studies
Vol. 3:1 (2021) ► pp. 1–32

Genre annotation for the Web

Text-external and text-internal perspectives

| University of Leeds
This paper describes a digital curation study aimed at comparing the composition of large Web corpora, such as enTenTen, ukWac or ruWac, by means of automatic text classification. First, the paper presents a Deep Learning model suitable for classifying texts from large Web corpora using a small number of communicative functions, such as Argumentation or Reporting. Second, it describes the results of applying the automatic classification model to these corpora and compares their composition. Finally, the paper introduces a framework for interpreting the results of automatic genre classification using linguistic features. The framework can help in comparing general reference corpora obtained from the Web and in comparing corpora across languages.
Keywords: automatic genre identification, Deep learning, interpreting neural networks
Article outline
  • 1.Introduction
    • 1.1Text-external communicative functions
    • 1.2Text-internal linguistic features
  • 2.Automatic genre identification
    • 2.1Text classification model
    • 2.2Datasets for training
    • 2.3Prediction accuracy
  • 3.Comparing large Web corpora
  • 4.Communicative functions vs linguistic features
    • 4.1Detection of linguistic features
    • 4.2Mapping linguistic features to functions
    • 4.3Linguistic features across languages
  • 5.Related studies on computational analysis of genres
  • 6.Conclusions and further work
  • Notes
  • References
Published online: 03 June 2021
https://doi.org/10.1075/rs.19015.sha
References
Adamzik, Kirsten
1995 Textsorten – Texttypologie. Eine Kommentierte Bibliographie. Münster: Nodus.Google Scholar
Argamon, Shlomo
2019 “Computational Register Analysis and Synthesis.” Register Studies 11.Google Scholar
Argamon, Shlomo, Casey Whitelaw, Paul Chase, Sobhan Raj Hota, Navendu Garg, and Shlomo Levitan
2007 “Stylistic Text Classification Using Functional Lexical Features.” Journal of the American Society for Information Science and Technology 58 (6). Wiley Online Library: 802–22. CrossrefGoogle Scholar
Baker, Mona
1996 “Corpus-Based Translation Studies: The Challenges That Lie Ahead.” In Terminology, Lsp and Translation: Studies in Language Engineering, edited by Harold Somers. John Benjamins. CrossrefGoogle Scholar
Baroni, Marco, and Silvia Bernardini
2006 “A New Approach to the Study of Translationese: Machine-Learning the Difference Between Original and Translated Text.” Literary and Linguistic Computing 21 (3): 259–74. CrossrefGoogle Scholar
Benko, Vladimír
2016 “Two Years of Aranea: Increasing Counts and Tuning the Pipeline.” In Proc Lrec. Portorož, Slovenia.Google Scholar
Biber, Douglas
1988 Variation Across Speech and Writing. Cambridge University Press. CrossrefGoogle Scholar
1995 Dimensions of Register Variation: A Cross-Linguistic Comparison. Cambridge University Press. CrossrefGoogle Scholar
Biber, Douglas, and Jesse Egbert
2016 “Register Variation on the Searchable Web: A Multi-Dimensional Analysis.” Journal of English Linguistics 44 (2): 95–137. CrossrefGoogle Scholar
Biber, Douglas, and Bethany Gray
2016 Grammatical Complexity in Academic English: Linguistic Change in Writing. Cambridge University Press. CrossrefGoogle Scholar
Cienki, Alan J.
1989 Spatial Cognition and the Semantics of Prepositions in English, Polish, and Russian. Vol. 2371. Sagner Munich. CrossrefGoogle Scholar
Conneau, Alexis, Shijie Wu, Haoran Li, Luke Zettlemoyer, and Veselin Stoyanov
2020 “Emerging Cross-Lingual Structure in Pretrained Language Models.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 6022–34. Online: Association for Computational Linguistics. CrossrefGoogle Scholar
Crowston, Kevin, Barbara Kwasnik, and Joseph Rubleske
2010 “Problems in the Use-Centered Development of a Taxonomy of Web Genres.” In Genres on the Web: Computational Models and Empirical Studies, edited by Alexander Mehler, Serge Sharoff, and Marina Santini. Springer. CrossrefGoogle Scholar
Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova
2018 “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” arXiv Preprint arXiv:1810.04805.Google Scholar
Evert, Stefan
2006 “How Random Is a Corpus? The Library Metaphor.” Zeitschrift Für Anglistik Und Amerikanistik 54 (2): 177–90. CrossrefGoogle Scholar
Ferraresi, Adriano, Eros Zanchetta, Silvia Bernardini, and Marco Baroni
2008 “Introducing and Evaluating ukWaC, a Very Large Web-Derived Corpus of English.” In The 4th Web as Corpus Workshop: Can We Beat Google? (At Lrec 2008). Marrakech. http://​clic​.cimec​.unitn​.it​/marco​/publications​/lrec2008​/lrec08​-ukwac​.pdf.
Forsyth, Richard, and Serge Sharoff
2014 “Document Dissimilarity Within and Across Languages: A Benchmarking Study.” Literary and Linguistic Computing 291: 6–22. CrossrefGoogle Scholar
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
2016 Deep Learning. MIT Press.Google Scholar
Görlach, M.
2004 Text Types and the History of English. Berlin: Walter de Gruyter. CrossrefGoogle Scholar
Gulordava, Kristina, Piotr Bojanowski, Edouard Grave, Tal Linzen, and Marco Baroni
2018 “Colorless Green Recurrent Networks Dream Hierarchically.” arXiv Preprint arXiv:1803.11138. CrossrefGoogle Scholar
Hearst, Marti A.
1997 “TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages.” Computational Linguistics 23 (1). MIT Press: 33–64.Google Scholar
Hosmer Jr, David W., Stanley Lemeshow, and Rodney X. Sturdivant
2013 Applied Logistic Regression. John Wiley & Sons. CrossrefGoogle Scholar
Jakubíček, Miloš, Adam Kilgarriff, Vojtěch Kovář, Pavel Rychly, and Vít Suchomel
2013 “The Tenten Corpus Family.” In Proc Corpus Linguistics Conference, 125–27. Lancaster.Google Scholar
Kanaris, Ioannis, and Efstathios Stamatatos
2007 “Webpage Genre Identification Using Variable-Length Character N-Grams.” http://​www​.icsd​.aegean​.gr​/lecturers​/Stamatatos​/papers​/ICTAI​-2007​.pdf. Crossref
Karlgren, Jussi, and Douglass Cutting
1994 “Recognizing Text Genres with Simple Metrics Using Discriminant Analysis.” In COLING ’94: Proc. of the 15th. International Conference on Computational Linguistics, 1071–5. Kyoto, Japan. CrossrefGoogle Scholar
Katinskaya, Anisya, and Serge Sharoff
2015 “Applying Multi-Dimensional Analysis to a Russian Webcorpus: Searching for Evidence of Genres.” In Proc Bsnlp. Sofia.Google Scholar
Kessler, Brett, Geoffrey Nunberg, and Hinrich Schütze
1997 “Automatic Detection of Text Genre.” In Proceedings of the 35〖^(th)〗 ACL/8〖^(th)〗 Eacl, 32–38.Google Scholar
Kilgarriff, Adam
2001 “The Web as Corpus.” In Proc Corpus Linguistics 2001. Lancaster. http://​www​.itri​.bton​.ac​.uk​/techreports​/ITRI​-01​-14​.abs​.html.
Kilgarriff, Adam, and Vít Suchomel
2013 “Web Spam.” In Proc Web as Corpus Workshop (Wac8) at Corpus Linguistics Conference. Lancaster.Google Scholar
Krippendorff, Klaus
2004 “Reliability in Content Analysis: Some Common Misconceptions and Recommendations.” Human Communication Research 30 (3): 411–33. CrossrefGoogle Scholar
Kunilovskaya, Maria, and Serge Sharoff
2019 “Building Functionally Similar Corpus Resources for Translation Studies.” In Proc Ranlp, 583–92. Varna.Google Scholar
Lee, David
2001 “Genres, Registers, Text Types, Domains, and Styles: Clarifying the Concepts and Navigating a Path Through the BNC Jungle.” Language Learning and Technology 5 (3): 37–72.Google Scholar
Liu, Bing, and Ian Lane
2016 “Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling.” arXiv Preprint arXiv:1609.01454. CrossrefGoogle Scholar
Matthiessen, Christian MIM.
2015 “Register in the Round: Registerial Cartography.” Functional Linguistics 2 (1): 1–48. CrossrefGoogle Scholar
Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean
2013 “Efficient Estimation of Word Representations in Vector Space.” In Proc. Workshop at Iclr’13.Google Scholar
Nesi, Hilary, and Sheena Gardner
2012 Genres Across the Disciplines: Student Writing in Higher Education. Cambridge: Cambridge University Press. CrossrefGoogle Scholar
Petrenz, Philipp, and Bonnie Webber
2010 “Stable Classification of Text Genres.” Computational Linguistics 34 (4): 285–93.Google Scholar
Santini, Marina, Alexander Mehler, and Serge Sharoff
2010 “Riding the Rough Waves of Genre on the Web.” In Genres on the Web: Computational Models and Empirical Studies, edited by Alexander Mehler, Serge Sharoff, and Marina Santini. Berlin/New York: Springer. CrossrefGoogle Scholar
Sharoff, Serge
2018 “Functional Text Dimensions for the Annotation of Web Corpora.” Corpora 13 (1): 65–95. CrossrefGoogle Scholar
Sharoff, Serge, Dirk Goldhahn, and Uwe Quasthoff
2017 “Frequency Dictionary: Russian.” In, 91:9–14. Frequency Dictionaries. Leipziger Universitätsverlag.Google Scholar
Sharoff, Serge, Zhili Wu, and Katja Markert
2010 “The Web Library of Babel: Evaluating Genre Collections.” In Proc Seventh Language Resources and Evaluation Conference, LREC. Malta.Google Scholar
Sinclair, John
1991 Corpus, Concordance and Collocation. Oxford: OUP.Google Scholar
Sinclair, John, and Jackie Ball
1996 “Preliminary Recommendations on Text Typology.” EAG-TCWG-TTYP/P. Expert Advisory Group on Language Engineering Standards document. http://​www​.ilc​.cnr​.it​/EAGLES96​/texttyp​/texttyp​.html.
Sorower, Mohammad S.
2010 “A Literature Survey on Algorithms for Multi-Label Learning.” Vol. 181. Oregon State University.Google Scholar
Stamatatos, Efstathios, George Kokkinakis, and Nikos Fakotakis
2000 “Automatic Text Categorization in Terms of Genre and Author.” Computational Linguistics 26 (4): 471–95. CrossrefGoogle Scholar
Straka, Milan, and Jana Straková
2017 “Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe.” In Proc Conll 2017 Shared Task, 88–99. Vancouver, Canada: Association for Computational Linguistics. CrossrefGoogle Scholar
Szmrecsanyi, Benedikt
2009 “Typological Parameters of Intralingual Variability: Grammatical Analyticity Versus Syntheticity in Varieties of English.” Language Variation and Change 21 (3). Cambridge University Press: 319–53. CrossrefGoogle Scholar
Yang, Yiming, and Jan O. Pedersen
1997 “A Comparative Study on Feature Selection in Text Categorization.” In Proc ICML, edited by Douglas H. Fisher, 412–20. Nashville, US.Google Scholar
Yogatama, Dani, Chris Dyer, Wang Ling, and Phil Blunsom
2017 “Generative and Discriminative Text Classification with Recurrent Neural Networks.” arXiv Preprint arXiv:1703.01898.Google Scholar